A Practical Guide to Surface Kinetic Monte Carlo Simulations

Esterification of Levulinic Acid to Ethyl Levulinate over Amberlyst-15 in Flow: Systematic Kinetic Model Discrimination and Parameter Estimation

An automated reactor platform was developed using LabVIEW to conduct preplanned experiments for the identification of a kinetic model for the esterification of Levulinic acid (LA) and ethanol over heterogeneous Amberlyst-15 catalyst. A Single Pellet String Reactor of 1.25 aspect ratio was used for this kinetic study, loaded with 0.1 g of 800 μm catalyst spheres, at flow rates 20-60 μL/min, temperatures 70-100 °C, and LA feed concentrations 0.8-1.6 M. An extensive library of power law, Langmuir-Hinshelwood-Hougen-Watson and Eley-Rideal models, was screened through the application of a general procedure for model discrimination and parameter estimation. The procedure, consisting of seven steps, was applied for the investigation of different design spaces and allowed for the reformulation of models to include temperature-dependent parameters, the former leading to an increase in model identifiability and the latter resulting in enhanced model fitting. The combination of experimental data sets including the addition of the reaction product (water) in the reactor inlet stream and the incorporation of temperature dependence in the adsorption coefficients' expression led to the identification of two suitable kinetic models out of 28 candidates (a Langmuir-Hinshelwood-Hougen-Watson and an Eley-Rideal model), both of which accounted for the adsorption of water on Amberlyst-15 and fitted the experimental data satisfactorily.

Particle Hopping and Coalescence of Supported Au Nanoparticles in Harsh Reactive Environments

Sintering of supported metal nanoparticles (NPs) is a general and important phenomenon in materials and catalysis science. A consensus view is that it takes place either via the Ostwald ripening (OR) or particle migration and coalescence (PMC) mechanism through the substrate, but how sintering occurs under high gas pressure and high temperature has not been addressed. Here, we perform millisecond-scale environmental kinetic Monte Carlo (EKMC) simulations combined with density functional theory (DFT) calculations to reveal a unique through-space sintering mechanism, particle hopping and coalescence (PHC). Under high CO pressure and high temperature, the coalescence of Au NPs takes place through NP hopping up from the anatase TiO2(101) substrate and mass transfer via the gas phase. When the sintered floating NP reaches a critical size, it spontaneously redeposits onto the substrate. This process is driven by the preference of interfacial Au atoms of small NPs to interact with CO rather than the substrate at a high CO chemical potential. The PHC mechanism implies that NP sintering and intersubstrate catalyst transfer may occur easier than expected during reactions and provides a distinct perspective to understand catalyst thermal deactivation under harsh operando conditions.

Automatic Process Exploration through Machine Learning Assisted Transition State Searches

We present an efficient automatic process explorer (APE) framework to overcome the reliance on human intuition to empirically establish relevant elementary processes of a given system, e.g., in prevalent kinetic Monte Carlo (kMC) simulations based on fixed process lists. Use of a fuzzy machine learning classification algorithm minimizes redundancy in the transition-state searches by driving them toward hitherto unexplored local atomic environments. APE application to island diffusion at a Pd(100) surface immediately reveals a large number of, up to now, disregarded low-barrier collective processes that lead to a significant increase in the kMC-determined island diffusivity as compared to classic surface hopping and exchange diffusion mechanisms.

Carbonation under Varying Humidity Conditions: A 3D Micro-Scale Kinetic Monte Carlo Approach

This paper investigates the impact of varying humidity conditions on the carbonation depth in hardened cement paste using a 3-dimensional microscale kinetic Monte Carlo (kMC) approach. The kMC algorithm effectively simulates the carbonation process by capturing the interplay between CO2 diffusion and relative humidity at the microscale, providing insights into macro trends that align with historical models. The study reveals that the maximum carbonation depth is achieved at relative humidity levels between 55 and 65%, where the balance between water and CO2 diffusion is optimized. At lower relative humidity levels (<55%), a lower carbonation depth is observed. Conversely, at higher relative humidity levels (>65%), increased water content impedes CO2 diffusion, resulting in reduced carbonation depth for cement paste. The kMC model demonstrates a parabolic relationship between relative humidity and carbonation depth. Time series analysis shows that Fick's law is consistently followed, with carbonation depth following the relationship x = k√t at constant relative humidity. The kMC also breaks down the event cycle which shows that after an equilibrium (in terms of rate of events) is achieved between CO2 and H2O at a relative humidity of 75%, a shift occurs in the dominance from reactive to transport processes at a relative humidity of 85%. These findings highlight the importance of humidity in influencing carbonation rates on the one hand and demonstrate the effectiveness of the kMC approach in simulating these complex interactions at the microscale on the other hand.

A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions

New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a cellular automaton simulation framework for predicting the time-dependent evolution of intermediate and product phases during solid-state reactions as a function of precursor choice and amount, reaction atmosphere, and heating profile. The simulation captures the effects of reactant particle spatial distribution, particle melting, and reaction atmosphere. Reaction rates based on rudimentary kinetics are estimated using density functional theory data from the Materials Project and machine learning estimators for the melting point and the vibrational entropy component of the Gibbs free energy. The resulting simulation framework allows for the prediction of the likely outcome of a reaction recipe before any experiments are performed. We analyze five experimental solid-state recipes for BaTiO3, CaZrN2, and YMnO3 found in the literature to illustrate the performance of the model in capturing reaction selectivity and reaction pathways as a function of temperature and precursor choice. This simulation framework offers an easier way to optimize existing recipes, aid in the identification of intermediates, and design effective recipes for yet unrealized inorganic solids in silico.

Computational Modeling of Reticular Materials: The Past, the Present, and the Future

Reticular materials rely on a unique building concept where inorganic and organic building units are stitched together giving access to an almost limitless number of structured ordered porous materials. Given the versatility of chemical elements, underlying nets, and topologies, reticular materials provide a unique platform to design materials for timely technological applications. Reticular materials have now found their way in important societal applications, like carbon capture to address climate change, water harvesting to extract atmospheric moisture in arid environments, and clean energy applications. Combining predictions from computational materials chemistry with advanced experimental characterization and synthesis procedures unlocks a design strategy to synthesize new materials with the desired properties and functions. Within this review, the current status of modeling reticular materials is addressed and supplemented with topical examples highlighting the necessity of advanced molecular modeling to design materials for technological applications. This review is structured as a templated molecular modeling study starting from the molecular structure of a realistic material towards the prediction of properties and functions of the materials. At the end, the authors provide their perspective on the past, present of future in modeling reticular materials and formulate open challenges to inspire future model and method developments.

Dynamic Adaptation of Active Site Driven by Dual-side Adsorption in Single-Atomic Catalysts During CO2 Electroreduction

Single-atom iron embedded in N-doped carbon (Fe-N-C) is among the most representative single-atomic catalysts (SACs) for electrochemical CO2 reduction reaction (CO2RR). Despite the simplicity of the active site, the CO2-to-CO mechanism on Fe-N-C remains controversial. Firstly, there is a long debate regarding the rate-determining step (RDS) of the reactions. Secondly, recent computational and experimental studies are puzzled by the fact that the CO-poisoned Fe centers still remain highly active at high potentials. Thirdly, there are ongoing challenges in elucidating the high selectivity of hydrogen evolution reaction (HER) over CO2RR at high potentials. In this work, we introduce a novel CO2RR mechanism on Fe-N-C, which was inspired by the dynamic of active sites in biological systems. By employing grand-canonical density functional theory and kinetic Monte-Carlo, we found that the RDS is not fixed but changes with the applied potential. We demonstrated that our proposed dual-side mechanisms could clarify the reason behind the high catalytic activity of CO-poisoned metal centers, as well as the high selectivity of HER over CO2RR at high potential. This study provides a fundamental explanation for long-standing puzzles of an important catalyst and calls for the importance of considering the dynamic of active sites in reaction mechanisms.

A multi-scale framework for predicting α-cyclodextrin assembly on polyethylene glycol axles

Controlling the distribution of rings on polymer axles, such as α-cyclodextrin (αCD) on polyethylene glycol (PEG), is paramount in imparting robust mechanical properties to slide-ring gels and polyrotaxane-based networks. Previous experiments demonstrated that the functionalization of polymer ends could modulate the coverage of αCDs on PEG. To explore the design rule, we propose a multi-scale framework for predicting αCD assembly on bare and functionalized PEG. Our approach combines all-atom molecular dynamics with two-dimensional (2D) umbrella sampling to compute the free energy landscapes of threading αCDs onto PEG with ends functionalized by various moieties. Together with the predicted free energy landscapes and a lattice treatment for αCD and polymer diffusion in dilute solutions, we construct a kinetic Monte Carlo (kMC) model to predict the number and intra-chain distribution of αCDs along the polymer axle. Our model predicts the effects of chain length, concentration, and threading barrier on the supramolecular structure of end-functionalized polypseudorotaxane. With simple modifications, our approach can be extended to explore the design rule of polyrotaxane-based materials with advanced network architectures.

Physical Mechanisms of Improved Performance of Scaffolded Zinc-Silver Battery Cathodes

Nanostructuring can greatly improve the electrode stability of rechargeable battery systems, such as Zn-Ag. In this report, we investigate the physical mechanisms by which nanostructuring alters structural properties of nanomaterials and thereby influences the structural stability of electrodes. We specifically consider the effects of Au-based nanoscaffolds on Ag. Through density functional theory calculations, we propose that the charge transfer at the Au-Ag interface can facilitate the formation of Ag2O from Ag centers. Structural analysis of samples prepared in this work shows that Ag2O is extruded from Au-Ag material in the form of sheets. Interestingly, the length scale of such protrusions depends on the Au mole fraction that significantly alters the system's overall structural morphology. We rationalize these findings via kinetic Monte Carlo simulations of the Ag2O growth process and highlight the role of Au as a heterogeneous nucleation center.

Modified Activation-Relaxation Technique (ARTn) Method Tuned for Efficient Identification of Transition States in Surface Reactions

Exploring potential energy surfaces (PES) is essential for unraveling the underlying mechanisms of chemical reactions and material properties. While the activation-relaxation technique (ARTn) is a state-of-the-art method for identifying saddle points on PES, it often faces challenges in complex energy landscapes, especially on surfaces. In this study, we introduce iso-ARTn, an enhanced ARTn method that incorporates constraints on an orthogonal hyperplane and employs an adaptive active volume. By leveraging a neural network potential (NNP) to conduct an exhaustive saddle point search on the Pt(111) surface with 0.3 monolayers of surface oxygen coverage, iso-ARTn achieves a success rate that is 8.2% higher than the original ARTn, with 40% fewer force calls. Moreover, this method effectively finds various saddle points without compromising the success rate. Combined with kinetic Monte Carlo simulations for event table construction, iso-ARTn with NNP demonstrates the capability to reveal structures consistent with experimental observations. This work signifies a substantial advancement in the investigation of PES, enhancing both the efficiency and breadth of saddle point searches.

Adaptive tau-leaping methods for microscopic-lattice kinetic Monte Carlo simulations

Traditional Kinetic Monte Carlo (KMC) approaches, rooted in Gillespie's stochastic simulation algorithm, become computationally demanding in systems with a large range of timescales. The goal of this work is to propose and study new adaptive lattice-KMC time integration strategies for spatially non-uniform systems. To that end, two novel adaptive tau-leaping methods and their corresponding time integration strategies are developed based on the idea of the "n-fold" direct KMC method. These strategies allow for the simultaneous execution of multiple reactions, advancing time by adaptively selected coarse increments. We present numerical experiments comparing the proposed methods with existing approaches in a catalytic surface kinetics application involving ammonia decomposition.

Comparative simulations of methanol steam reforming on PdZn alloy using kinetic Monte Carlo and mean-field microkinetic model

Methanol steam reforming (MSR) is an attractive route for producing clean energy hydrogen. PdZn alloys are extensively studied as potential MSR catalysts for their stability and high CO2 selectivity. Here, we investigated the reaction mechanism using density functional calculations, mean-field microkinetic modeling (MF-MKM), and kinetic Monte Carlo (kMC) simulations. To overcome the over-underestimation of CO2 selectivity by log-kMC, an ads-kMC algorithm is proposed in which the adsorption/desorption rate constants were reduced under certain requirements and the diffusion process was treated by redistributing surface species each time an event occured. The simulations show that the dominant pathway to CO2 at low temperatures is CH3OH → CH3O → CH2O → H2COOH → H2COO → HCOO → CO2. The ads-kMC predicted OH coverage is 2-3 times that of MF-MKM, while they produce similar coverage for other species. Analyses indicate that surface OH promotes the dehydrogenation of CH3OH, CH3O, and H2COOH significantly and plays a key role in the MSR process. The dissociation of water/methanol is the most important rate-limiting/rate-inhibiting step. The CO2 selectivity obtained by the two methods is close to each other and consistent with the experimental trend with temperature. Generally, the ads-kMC results agree with the MF-MKM ones, supporting the previous finding that kMC and MF-MKM predict similar results if the diffusion is very fast and adsorbate interactions are neglected. The present study sheds light on the MSR process on PdZn alloys, and the proposed scheme to overcome the stiff problems in kMC simulations is worthy of being extended to other systems.

On-the-fly kinetic Monte Carlo simulations with neural network potentials for surface diffusion and reaction

We develop an adaptive scheme in the kinetic Monte Carlo simulations, where the adsorption and activation energies of all elementary steps, including the effects of other adsorbates, are evaluated "on-the-fly" by employing the neural network potentials. The configurations and energies evaluated during the simulations are stored for reuse when the same configurations are sampled in a later step. The present scheme is applied to hydrogen adsorption and diffusion on the Pd(111) and Pt(111) surfaces and the CO oxidation reaction on the Pt(111) surface. The effects of interactions between adsorbates, i.e., adsorbate-adsorbate lateral interactions, are examined in detail by comparing the simulations without considering lateral interactions. This study demonstrates the importance of lateral interactions in surface diffusion and reactions and the potential of our scheme for applications in a wide variety of heterogeneous catalytic reactions.

Dynamics accelerate the kinetics of ion diffusion through channels: Continuous-time random walk models beyond the mean field approximation

Ion channels are proteins that play a significant role in physiological processes, including neuronal excitability and signal transduction. However, the precise mechanisms by which these proteins facilitate ion diffusion through cell membranes are not well understood. This is because experimental techniques to characterize ion channel activity operate on a time scale too large to understand the role of the various protein conformations on diffusion. Meanwhile, computational approaches operate on a time scale too short to rationalize the observed behavior at the microscopic scale. In this paper, we present a continuous-time random walk model that aims to bridge the scales between the atomistic models of ion channels and the experimental measurement of their conductance. We show how diffusion slows down in complex systems by using 3D lattices that map out the pore geometry of two channels: Nav1.7 and gramicidin. We also introduce spatial and dynamic site disorder to account for system heterogeneity beyond the mean field approximation. Computed diffusion coefficients show that an increase in spatial disorder slows down diffusion kinetics, while dynamic disorder has the opposite effect. Our results imply that microscopic or phenomenological models based on the potential of mean force data overlook the functional importance of protein dynamics on ion diffusion through channels.

Brownian Electric Bubble Quasiparticles

Recent works on electric bubbles (including the experimental demonstration of electric skyrmions) constitute a breakthrough akin to the discovery of magnetic skyrmions some 15 years ago. So far research has focused on obtaining and visualizing these objects, which often appear to be immobile (pinned) in experiments. Thus, critical aspects of magnetic skyrmions-e.g., their quasiparticle nature, Brownian motion-remain unexplored (unproven) for electric bubbles. Here we use predictive atomistic simulations to investigate the basic dynamical properties of these objects in pinning-free model systems. We show that it is possible to find regimes where the electric bubbles can present long lifetimes (∼ns) despite being relatively small (diameter <2  nm). Additionally, we find that they can display stochastic dynamics with large and highly tunable diffusion constants. We thus establish the quasiparticle nature of electric bubbles and put them forward for the physical effects and applications (e.g., in token-based probabilistic computing) considered for magnetic skyrmions.

Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization

High entropy alloys (HEAs) are a highly promising class of materials for electrocatalysis as their unique active site distributions break the scaling relations that limit the activity of conventional transition metal catalysts. Existing Bayesian optimization (BO)-based virtual screening approaches focus on catalytic activity as the sole objective and correspondingly tend to identify promising materials that are unlikely to be entropically stabilized. Here, we overcome this limitation with a multiobjective BO framework for HEAs that simultaneously targets activity, cost-effectiveness, and entropic stabilization. With diversity-guided batch selection further boosting its data efficiency, the framework readily identifies numerous promising candidates for the oxygen reduction reaction that strike the balance between all three objectives in hitherto unchartered HEA design spaces comprising up to 10 elements.

Area-selective atomic layer deposition on 2D monolayer lateral superlattices

The advanced patterning process is the basis of integration technology to realize the development of next-generation high-speed, low-power consumption devices. Recently, area-selective atomic layer deposition (AS-ALD), which allows the direct deposition of target materials on the desired area using a deposition barrier, has emerged as an alternative patterning process. However, the AS-ALD process remains challenging to use for the improvement of patterning resolution and selectivity. In this study, we report a superlattice-based AS-ALD (SAS-ALD) process using a two-dimensional (2D) MoS2-MoSe2 lateral superlattice as a pre-defining template. We achieved a minimum half pitch size of a sub-10 nm scale for the resulting AS-ALD on the 2D superlattice template by controlling the duration time of chemical vapor deposition (CVD) precursors. SAS-ALD introduces a mechanism that enables selectivity through the adsorption and diffusion processes of ALD precursors, distinctly different from conventional AS-ALD method. This technique facilitates selective deposition even on small pattern sizes and is compatible with the use of highly reactive precursors like trimethyl aluminum. Moreover, it allows for the selective deposition of a variety of materials, including Al2O3, HfO2, Ru, Te, and Sb2Se3.

What Makes On-Chip Microdevices Stand Out in Electrocatalysis?

Clean and sustainable energy conversion and storage through electrochemistry shows great promise as an alternative to traditional fuel or fossil-consumption energy systems. With regards to practical and high-efficient electrochemistry application, the rational design of active sites and the accurate description of mechanism remain a challenge. Toward this end, in this Perspective, a unique on-chip micro/nano device coupling nanofabrication and low-dimensional electrochemical materials is presented, in which material structure analysis, field-effect regulation, in situ monitoring, and simulation modeling are highlighted. The critical mechanisms that influence electrochemical response are discussed, and how on-chip micro/nano device distinguishes itself is emphasized. The key challenges and opportunities of on-chip electrochemical platforms are also provided through the Perspective.

Importance of Site Diversity and Connectivity in Electrochemical CO Reduction on Cu

Electrochemical CO2 reduction on Cu is a promising approach to produce value-added chemicals using renewable feedstocks, yet various Cu preparations have led to differences in activity and selectivity toward single and multicarbon products. Here, we find, surprisingly, that the effective catalytic activity toward ethylene improves when there is a larger fraction of less active sites acting as reservoirs of *CO on the surface of Cu nanoparticle electrocatalysts. In an adaptation of chemical transient kinetics to electrocatalysis, we measure the dynamic response of a gas diffusion electrode (GDE) cell when the feed gas is abruptly switched between Ar (inert) and CO. When switching from Ar to CO, CO reduction (COR) begins promptly, but when switching from CO to Ar, COR can be maintained for several seconds (delay time) despite the absence of the CO reactant in the gas phase. A three-site microkinetic model captures the observed dynamic behavior and shows that Cu catalysts exhibiting delay times have a less active *CO reservoir that exhibits fast diffusion to active sites. The observed delay times and the estimated *CO reservoir sizes are affected by catalyst preparation, applied potential, and microenvironment (electrolyte cation identity, electrolyte pH, and CO partial pressure). Notably, we estimate that the *CO reservoir surface coverage can be as high as 88 ± 7% on oxide-derived Cu (OD-Cu) at high overpotentials (-1.52 V vs SHE) and this increases in reservoir coverage coincide with increased turnover frequencies to ethylene. We also estimate that *CO can travel substantial distances (up to 10s of nm) prior to desorption or reaction. It appears that active C-C coupling sites by themselves do not control selectivity to C2+ products in electrochemical COR; the supply of CO to those sites is also a crucial factor. More generally, the overall activity of Cu electrocatalysts cannot be approximated from linear combinations of individual site activities. Future designs must consider the diversity of the catalyst network and account for intersite transportation pathways.

Residual cells and nutrient availability guide wound healing in bacterial biofilms

Biofilms are multicellular heterogeneous bacterial communities characterized by social-like division of labor, and remarkable robustness with respect to external stresses. Increasingly often an analogy between biofilms and arguably more complex eukaryotic tissues is being drawn. One illustrative example of where this analogy can be practically useful is the process of wound healing. While it has been extensively studied in eukaryotic tissues, the mechanism of wound healing in biofilms is virtually unexplored. Combining experiments in Bacillus subtilis bacteria, a model organism for biofilm formation, and a lattice-based theoretical model of biofilm growth, we studied how biofilms recover after macroscopic damage. We suggest that nutrient gradients and the abundance of proliferating cells are key factors augmenting wound closure. Accordingly, in the model, cell quiescence, nutrient fluxes, and biomass represented by cells and self-secreted extracellular matrix are necessary to qualitatively recapitulate the experimental results for damage repair. One of the surprising experimental findings is that residual cells, persisting in a damaged area after removal of a part of the biofilm, prominently affect the healing process. Taken together, our results outline the important roles of nutrient gradients and residual cells on biomass regrowth on macroscopic scales of the whole biofilm. The proposed combined experiment-simulation framework opens the way to further investigate the possible relation between wound healing, cell signaling and cell phenotype alternation in the local microenvironment of the wound.

Dissolution enables dolomite crystal growth near ambient conditions

Crystals grow in supersaturated solutions. A mysterious counterexample is dolomite CaMg(CO3)2, a geologically abundant sedimentary mineral that does not readily grow at ambient conditions, not even under highly supersaturated solutions. Using atomistic simulations, we show that dolomite initially precipitates a cation-disordered surface, where high surface strains inhibit further crystal growth. However, mild undersaturation will preferentially dissolve these disordered regions, enabling increased order upon reprecipitation. Our simulations predict that frequent cycling of a solution between supersaturation and undersaturation can accelerate dolomite growth by up to seven orders of magnitude. We validated our theory with in situ liquid cell transmission electron microscopy, directly observing bulk dolomite growth after pulses of dissolution. This mechanism explains why modern dolomite is primarily found in natural environments with pH or salinity fluctuations. More generally, it reveals that the growth and ripening of defect-free crystals can be facilitated by deliberate periods of mild dissolution.

Knowledge-driven design of solid-electrolyte interphases on lithium metal via multiscale modelling

Due to its high energy density, lithium metal is a promising electrode for future energy storage. However, its practical capacity, cyclability and safety heavily depend on controlling its reactivity in contact with liquid electrolytes, which leads to the formation of a solid electrolyte interphase (SEI). In particular, there is a lack of fundamental mechanistic understanding of how the electrolyte composition impacts the SEI formation and its governing processes. Here, we present an in-depth model-based analysis of the initial SEI formation on lithium metal in a carbonate-based electrolyte. Thereby we reach for significantly larger length and time scales than comparable molecular dynamic studies. Our multiscale kinetic Monte Carlo/continuum model shows a layered, mostly inorganic SEI consisting of LiF on top of Li2CO3 and Li after 1 µs. Its formation is traced back to a complex interplay of various electrolyte and salt decomposition processes. We further reveal that low local Li+ concentrations result in a more mosaic-like, partly organic SEI and that a faster passivation of the lithium metal surface can be achieved by increasing the salt concentration. Based on this we suggest design strategies for SEI on lithium metal and make an important step towards knowledge-driven SEI engineering.

Surface coverage dynamics for reversible dissociative adsorption on finite linear lattices

Dissociative adsorption onto a surface introduces dynamic correlations between neighboring sites not found in non-dissociative absorption. We study surface coverage dynamics where reversible dissociative adsorption of dimers occurs on a finite linear lattice. We derive analytic expressions for the equilibrium surface coverage as a function of the number of reactive sites, N, and the ratio of the adsorption and desorption rates. Using these results, we characterize the finite size effect on the equilibrium surface coverage. For comparable N's, the finite size effect is significantly larger when N is even than when N is odd. Moreover, as N increases, the size effect decays more slowly in the even case than in the odd case. The finite-size effect becomes significant when adsorption and desorption rates are considerably different. These finite-size effects are related to the number of accessible configurations in a finite system where the odd-even dependence arises from the limited number of accessible configurations in the even case. We confirm our analytical results with kinetic Monte Carlo simulations. We also analyze the surface-diffusion case where adsorbed atoms can hop into neighboring sites. As expected, the odd-even dependence disappears because more configurations are accessible in the even case due to surface diffusion.

Aggregation of Size-Selected Oxide Clusters Deposited onto Au(111)

Kinetic Monte Carlo (kMC) simulations along with density functional theory (DFT) calculations were used to investigate the aggregation of size-selected Nb3Oy (y = 5, 6, 7) clusters deposited onto the Au(111) surface. Recent STM experiments showed that the cluster binding sites and sizes of the cluster assemblies on the Nb3Oy/Au(111) surfaces strongly depend on the stoichiometry of the clusters, i.e., the oxygen-to-niobium ratio. To better understand the origins of these differences, kMC simulations of the nucleation and growth of cluster assemblies were performed using energy barriers for diffusion and intercluster interactions estimated from DFT calculations of cluster binding and dimerization energies, respectively. Comparisons of the kMC simulations with STM images of the as-deposited Nb3Oy/Au(111) surfaces at RT and after high temperature annealing were used to further optimize the energetics and gauge the importance of nearest neighbor interactions. The kMC simulations demonstrate that the assembly of Nb3Oy clusters on Au(111) are largely controlled by the magnitude of the barriers for diffusion and interparticle-bond formation, while changes at higher temperatures are sensitive to the binding energies between nearest neighbors. Simulations for the Nb3O5 and Nb3O6 clusters, which exhibit smaller cluster assembly sizes in STM, required larger diffusion barriers as well as different barriers for interparticle binding, which reflected differences in DFT calculated dimerization energies. The results demonstrate the effectiveness of combined DFT and kMC calculations for understanding how the stoichiometry affects the aggregation of small oxide clusters on a metal surface.

Tube geometry controls protein cluster conformation and stability on the endoplasmic reticulum surface

The endoplasmic reticulum (ER), a cellular organelle that forms a cell-spanning network of tubes and sheets, is an important location of protein synthesis and folding. When the ER experiences sustained unfolded protein stress, IRE1 proteins embedded in the ER membrane activate and assemble into clusters as part of the unfolded protein response (UPR). We use kinetic Monte Carlo simulations to explore IRE1 clustering dynamics on the surface of ER tubes. While initially growing clusters are approximately round, once a cluster is sufficiently large a shorter interface length can be achieved by 'wrapping' around the ER tube. A wrapped cluster can grow without further interface length increases. Relative to wide tubes, narrower tubes enable cluster wrapping at smaller cluster sizes. Our simulations show that wrapped clusters on narrower tubes grow more rapidly, evaporate more slowly, and require a lower protein concentration to grow compared to equal-area round clusters on wider tubes. These results suggest that cluster wrapping, facilitated by narrower tubes, could be an important factor in the growth and stability of IRE1 clusters and thus impact the persistence of the UPR, connecting geometry to signaling behavior. This work is consistent with recent experimental observations of IRE1 clusters wrapped around narrow tubes in the ER network.

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations

Within this Perspective, we critically reflect on the role of first-principles molecular dynamics (MD) simulations in unraveling the catalytic function within zeolites under operating conditions. First-principles MD simulations refer to methods where the dynamics of the nuclei is followed in time by integrating the Newtonian equations of motion on a potential energy surface that is determined by solving the quantum-mechanical many-body problem for the electrons. Catalytic solids used in industrial applications show an intriguing high degree of complexity, with phenomena taking place at a broad range of length and time scales. Additionally, the state and function of a catalyst critically depend on the operating conditions, such as temperature, moisture, presence of water, etc. Herein we show by means of a series of exemplary cases how first-principles MD simulations are instrumental to unravel the catalyst complexity at the molecular scale. Examples show how the nature of reactive species at higher catalytic temperatures may drastically change compared to species at lower temperatures and how the nature of active sites may dynamically change upon exposure to water. To simulate rare events, first-principles MD simulations need to be used in combination with enhanced sampling techniques to efficiently sample low-probability regions of phase space. Using these techniques, it is shown how competitive pathways at operating conditions can be discovered and how broad transition state regions can be explored. Interestingly, such simulations can also be used to study hindered diffusion under operating conditions. The cases shown clearly illustrate how first-principles MD simulations reveal insights into the catalytic function at operating conditions, which could not be discovered using static or local approaches where only a few points are considered on the potential energy surface (PES). Despite these advantages, some major hurdles still exist to fully integrate first-principles MD methods in a standard computational catalytic workflow or to use the output of MD simulations as input for multiple length/time scale methods that aim to bridge to the reactor scale. First of all, methods are needed that allow us to evaluate the interatomic forces with quantum-mechanical accuracy, albeit at a much lower computational cost compared to currently used density functional theory (DFT) methods. The use of DFT limits the currently attainable length/time scales to hundreds of picoseconds and a few nanometers, which are much smaller than realistic catalyst particle dimensions and time scales encountered in the catalysis process. One solution could be to construct machine learning potentials (MLPs), where a numerical potential is derived from underlying quantum-mechanical data, which could be used in subsequent MD simulations. As such, much longer length and time scales could be reached; however, quite some research is still necessary to construct MLPs for the complex systems encountered in industrially used catalysts. Second, most currently used enhanced sampling techniques in catalysis make use of collective variables (CVs), which are mostly determined based on chemical intuition. To explore complex reactive networks with MD simulations, methods are needed that allow the automatic discovery of CVs or methods that do not rely on a priori definition of CVs. Recently, various data-driven methods have been proposed, which could be explored for complex catalytic systems. Lastly, first-principles MD methods are currently mostly used to investigate local reactive events. We hope that with the rise of data-driven methods and more efficient methods to describe the PES, first-principles MD methods will in the future also be able to describe longer length/time scale processes in catalysis. This might lead to a consistent dynamic description of all steps-diffusion, adsorption, and reaction-as they take place at the catalyst particle level.

Steady state of a two-species annihilation process with separated reactants

We describe the steady state of the annihilation process of a one-dimensional system of two initially separated reactants A and B. The parameters that define the dynamical behavior of the system are the diffusion constant, the reaction rate, and the deposition rate. Depending on the ratio between those parameters, the system exhibits a crossover between a diffusion-limited (DL) regime and a reaction-limited (RL) regime. We found that a key quantity to describe the reaction process in the system is the probability p(x_{A},x_{B}) to find the rightmost A (RMA) particle and the leftmost B (LMB) particle at the positions x_{A} and x_{B}, respectively. The statistical behavior of the system in both regimes is described using the density of particles, the gap length distribution x_{B}-x_{A}, the marginal probabilities p_{A}(x_{A}) and p_{B}(x_{B}), and the reaction kernel. For both regimes, this kernel can be approximated by using p(x_{A},x_{B}). We found an excellent agreement between the numerical and analytical results for all calculated quantities despite the reaction process being quite different in both regimes. In the DL regime, the reaction kernel can be approximated by the probability to find the RMA and LMB particles in adjacent sites. In the RL regime, the kernel depends on the marginal probabilities p_{A}(x_{A}) and p_{B}(x_{B}).

Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization

The most in-demand instrumental methods for new functional nanomaterial diagnostics employ synchrotron radiation, which is used to determine a material's electronic and local atomic structure. The high time and resource costs of researching at international synchrotron radiation centers and the problems involved in developing an optimal strategy and in planning the control of the experiments are acute. One possible approach to solving these problems involves the use of deep reinforcement learning agents. However, this approach requires the creation of a special environment that provides a reliable level of response to the agent's actions. As the physical experimental environment of nanocatalyst diagnostics is potentially a complex multiscale system, there are no unified comprehensive representations that formalize the structure and states as a single digital model. This study proposes an approach based on the decomposition of the experimental system into the original physically plausible nodes, with subsequent merging and optimization as a metagraphic representation with which to model the complex multiscale physicochemical environments. The advantage of this approach is the possibility to directly use the numerical model to predict the system states and to optimize the experimental conditions and parameters. Additionally, the obtained model can form the basic planning principles and allow for the optimization of the search for the optimal strategy with which to control the experiment when it is used as a training environment to provide different abstraction levels of system state reactions.

CrystalClear: an open, modular protocol for predicting molecular crystal growth from solution

We present a new protocol for the prediction of free energies that determine the growth of sites in molecular crystals for subsequent use in Monte Carlo simulations using tools such as CrystalGrower [Hill et al., Chemical Science, 2021, 12, 1126-1146]. Key features of the proposed approach are that it requires minimal input, namely the crystal structure and solvent only, and provides automated, rapid generation of the interaction energies. The constituent components of this protocol, namely interactions between molecules (growth units) in the crystal, solvation contributions and treatment of long-range interactions are described in detail. The power of this method is shown via prediction of crystal shapes for ibuprofen grown from ethanol, ethyl acetate, toluene and acetonitrile, adipic acid grown from water, and five polymorphs (ON, OP, Y, YT04 and R) of ROY (5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile), with promising results. The predicted energies may be used directly or subsequently refined against experimental data, facilitating insight into the interactions governing crystal growth, while also providing a prediction of the solubility of the material. The protocol has been implemented in standalone, open-source software made available alongside this publication.

Spiral growth of multicomponent crystals: theoretical aspects

This paper presents recent advances in the theory of multicomponent crystal growth from gas or solution, focusing on the most common step-flow mechanisms: Burton-Cabrera-Frank, Chernov, and Gilmer-Ghez-Cabrera. Analytical expressions for the spiral crystal growth rate are presented, taking into account the properties of all species involved in the growth process. The paper also outlines theoretical approaches to consider these mechanisms in multicomponent systems, providing a foundation for future developments and exploration of previously unexplored effects. Some special cases are discussed, including the formation of nanoislands of pure components on the surface and their self-organization, the impact of applied mechanical stress on the growth rate, and the mechanisms of its influence on growth kinetics. The growth due to chemical reactions on the surface is also considered. Possible future directions for developing the theory are outlined. A brief overview of numerical approaches and software codes that are useful in theoretical studies of crystal growth is also given.

Assessing the Impact of Adlayer Description Fidelity on Theoretical Predictions of Coking on Ni(111) at Steam Reforming Conditions

Methane steam reforming is an important industrial process for hydrogen production, employing Ni as a low-cost, highly active catalyst, which, however, suffers from coking due to methane cracking. Coking is the accumulation of a stable poison over time, occurring at high temperatures; thus, to a first approximation, it can be treated as a thermodynamic problem. In this work, we developed an Ab initio kinetic Monte Carlo (KMC) model for methane cracking on Ni(111) at steam reforming conditions. The model captures C-H activation kinetics in detail, while graphene sheet formation is described at the level of thermodynamics, to obtain insights into the "terminal (poisoned) state" of graphene/coke within reasonable computational times. We used cluster expansions (CEs) of progressively higher fidelity to systematically assess the influence of effective cluster interactions between adsorbed or covalently bonded C and CH species on the "terminal state" morphology. Moreover, we compared the predictions of KMC models incorporating these CEs into mean-field microkinetic models in a consistent manner. The models show that the "terminal state" changes significantly with the level of fidelity of the CEs. Furthermore, high-fidelity simulations predict C-CH island/rings that are largely disconnected at low temperatures but completely encapsulate the Ni(111) surface at high temperatures.

Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials

While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.

An Atomistic Model of Field-Induced Resistive Switching in Valence Change Memory

In valence change memory (VCM) cells, the conductance of an insulating switching layer is reversibly modulated by creating and redistributing point defects under an external field. Accurately simulating the switching dynamics of these devices can be difficult due to their typically disordered atomic structures and inhomogeneous arrangements of defects. To address this, we introduce an atomistic framework for modeling VCM cells. It combines a stochastic kinetic Monte Carlo approach for atomic rearrangement with a quantum transport scheme, both parametrized at the ab initio level by using inputs from density functional theory. Each of these steps operates directly on the underlying atomic structure. The model thus directly relates the energy landscape and electronic structure of the device to its switching characteristics. We apply this model to simulate field-induced nonvolatile switching between high- and low-resistance states in a TiN/HfO₂/Ti/TiN stack and analyze both the kinetics and stochasticity of the conductance transitions. We also resolve the atomic nature of current flow resulting from the valence change mechanism, finding that conductive paths are formed between the undercoordinated Hf atoms neighboring oxygen vacancies. The model developed here can be applied to different material systems to evaluate their resistive switching potential, both for use as conventional memory cells and as neuromorphic computing primitives.

Achieving Optimal Paper Properties: A Layered Multiscale kMC and LSTM-ANN-Based Control Approach for Kraft Pulping

The growing demand for various types of paper highlights the importance of optimizing the kraft pulping process to achieve desired paper properties. This work proposes a novel multiscale model to optimize the kraft pulping process and obtain desired paper properties. The model combines mass and energy balance equations with a layered kinetic Monte Carlo (kMC) algorithm to predict the degradation of wood chips, the depolymerization of cellulose, and the spatio-temporal evolution of the Kappa number and cellulose degree of polymerization (DP). A surrogate LSTM-ANN model is trained on data generated from the multiscale model under different operating conditions, dealing with both time-varying and time-invariant inputs, and an LSTM-ANN-based model predictive controller is designed to achieve desired set-point values of the Kappa number and cellulose DP while considering process constraints. The results show that the LSTM-ANN-based controller is able to drive the process to desired set-point values with the use of a computationally faster surrogate model with high accuracy and low offset.

Nature of Catalytic Behavior of Cobalt Oxides for CO2 Hydrogenation

Cobalt oxide (CoO x ) catalysts are widely applied in CO2 hydrogenation but suffer from structural evolution during the reaction. This paper describes the complicated structure-performance relationship under reaction conditions. An iterative approach was employed to simulate the reduction process with the help of neural network potential-accelerated molecular dynamics. Based on the reduced models of catalysts, a combined theoretical and experimental study has discovered that CoO(111) provides active sites to break C-O bonds for CH4 production. The analysis of the reaction mechanism indicated that the C-O bond scission of *CH2O species plays a key role in producing CH4. The nature of dissociating C-O bonds is attributed to the stabilization of *O atoms after C-O bond cleavage and the weakening of C-O bond strength by surface-transferred electrons. This work may offer a paradigm to explore the origin of performance over metal oxides in heterogeneous catalysis.

Ensemble Effects in Adsorbate-Adsorbate Interactions in Microkinetic Modeling

Adsorbates on a surface experience lateral interactions that result in a distribution of adsorption energies. The adsorbate-adsorbate interactions are known to affect the kinetics of surface reactions, which motivates efforts to develop models that accurately account for the interactions. Here, we use density functional theory (DFT) calculations combined with Monte Carlo simulations to investigate how the distribution of adsorbates affects adsorption and desorption of CO from Pt(111). We find that the mean of the average adsorption energy determines the adsorption process, whereas the desorption process can be described by the low energy part of the adsorbate stability distribution. The simulated results are in very good agreement with calorimetry and temperature-programmed desorption experiments and provide a guideline of how to include adsorbate-adsorbate interactions in DFT-based mean-field kinetic models.

A probabilistic microkinetic modeling framework for catalytic surface reactions

We present a probabilistic microkinetic modeling (MKM) framework that incorporates the short-ranged order (SRO) evolution for adsorbed species (adspecies) on a catalyst surface. The resulting model consists of a system of ordinary differential equations. Adsorbate-adsorbate interactions, surface diffusion, adsorption, desorption, and catalytic reaction processes are included. Assuming that the adspecies ordering/arrangement is accurately described by the SRO parameters, we employ the reverse Monte Carlo (RMC) method to extract the relevant local environment probability distributions and pass them to the MKM. The reaction kinetics is faithfully captured as accurately as the kinetic Monte Carlo (KMC) method but with a computational time requirement of few seconds on a standard desktop computer. KMC, on the other hand, can require several days for the examples discussed. The framework presented here is expected to provide the basis for wider application of the RMC-MKM approach to problems in computational catalysis, electrocatalysis, and material science.

Emergence of sector and spiral patterns from a two-species mutualistic cross-feeding model

The ubiquitous existence of microbial communities marks the importance of understanding how species interact within the community to coexist and their spatial organization. We study a two-species mutualistic cross-feeding model through a stochastic cellular automaton on a square lattice using kinetic Monte Carlo simulation. Our model encapsulates the essential dynamic processes such as cell growth, and nutrient excretion, diffusion and uptake. Focusing on the interplay among nutrient diffusion and individual cell division, we discover three general classes of colony morphology: co-existing sectors, co-existing spirals, and engulfment. When the cross-feeding nutrient is widely available, either through high excretion or fast diffusion, a stable circular colony with alternating species sector emerges. When the consumer cells rely on being spatially close to the producers, we observe a stable spiral. We also see one species being engulfed by the other when species interfaces merge due to stochastic fluctuation. By tuning the diffusion rate and the growth rate, we are able to gain quantitative insights into the structures of the sectors and the spirals.

Long-Term Evolution of Vacancies in Large-Area Graphene

Devices based on two-dimensional (2D) materials such as graphene and molybdenum disulfide have shown extraordinary potential in physics, nanotechnology, and electronics. The performances of these applications are heavily affected by defects in utilized materials. Although great efforts have been spent in studying the formation and property of various defects in 2D materials, the long-term evolution of vacancies is still unclear. Here, using a designed program based on the kinetic Monte Carlo method, we systematically investigate the vacancy evolution in monolayer graphene on a long-time and large spatial scale, focusing on the variation of the distribution of different vacancy types. In most cases, the vacancy distribution remains nearly unchanged during the whole evolution, and most of the evolution events are vacancy migrations with a few being coalescences, while it is extremely difficult for multiple vacancies to dissolve. The probabilities of different categories of vacancy evolutions are determined by their reaction rates, which, in turn, depend on corresponding energy barriers. We further study the influences of different factors such as the energy barrier for vacancy migration, coalescence, and dissociation on the evolution, and the coalescence energy barrier is found to be dominant. These findings indicate that vacancies (also subnanopores) in graphene are thermodynamically stable for a long period of time, conducive to subsequent characterizations or applications. Besides, this work provides hints to tune the ultimate vacancy distribution by changing related factors and suggests ways to study the evolution of other defects in various 2D materials.

Anomalous dewetting growth of Si on Ag(111)

We demonstrate the novel growth of silicene grown on Ag(111) using STM and reveal the mechanism with KMC simulation. Our STM study shows that after the complete formation of the first layer of silicene, it is transformed into bulk Si with the reappearance of the bare Ag surface. This dewetting (DW) during the epitaxial growth is an exception in the conventional growth behavior. Our KMC simulation reproduces DW by taking into account the differences in the activation energies of Si atoms on Ag, silicene, and bulk Si. The growth modes change depending on the activation energy of the diffusion, temperature, and deposition rate, highlighting the importance of kinetics in growing metastable 2D materials.

Gaining Insight into the Electrochemical Interface Dynamics in an Organic Redox Flow Battery with a Kinetic Monte Carlo Approach

Finding low-cost and nontoxic redox couples for organic redox flow batteries is challenging due to unrevealed reaction mechanisms and side reactions. In this study, a 3D kinetic Monte Carlo model to study the electrode-anolyte interface of a methyl viologen-based organic redox flow battery is presented. This model captures various electrode processes, such as ionic displacement and degradation of active materials. The workflow consists of input parameters obtained from density functional theory calculations, a kinetic Monte Carlo algorithm to simulate the discharging process, and an electric double layer model to account for the electric field distribution near the electrode surface. Galvanostatic discharge is simulated at different anolyte concentrations and input current densities, which demonstrate that the model captured the formation of the electrical double layer due to ionic transport. The simulated electrochemical kinetics (potential, charge density) are found to be in agreement with the Nernst equation and the obtained EDL structure corresponded with published molecular dynamics results. The model's flexibility allows further applications of simulating the behavior of different redox couples and makes it possible to consider other molecular-scale phenomena. This study paves the way for computational screening of active species by assessing their potential kinetics in electrochemical environments.

First-principles modeling of the highly dynamical surface structure of a MoS2 catalyst with S-vacancies

Vacancy sites, e.g., S-vacancies, are essential for the performance of MoS₂ catalysts. As earlier studies have revealed that the size and shape of the S-vacancies may affect the catalytic activity, we have studied the behavior and mobility of such vacancies on MoS₂ using DFT calculations and kinetic Monte-Carlo (kMC) simulations. The diffusion barriers for the S-vacancies are highly dependent on the immediate environment: isolated single S-vacancies are found to be immobile. In contrast, small nS-vacancies formed from n = 2 to 5 neighboring S-vacancies are often highly dynamic systems that can move within a confined area. Large extended nS-vacancies are generally unstable and transform quickly into alternating patterns of S-atoms and vacancy sites. These results illustrate the importance of recognizing MoS₂ (but also other catalysts) as dynamic structures when trying to tune their catalytic performances by introducing specific defect structures.

Unveiling the Au Surface Reconstruction in a CO Environment by Surface Dynamics and Ab Initio Thermodynamics

Surface reconstruction changes the atomic configuration of the metal surface and thus alters its intrinsic physical and chemical properties. Recent in situ experiments have shown a variety of surface reconstructions under reaction conditions, but how to effectively predict and characterize these structures remains challenging. Herein, we combine a DFT-based kinetic Monte Carlo simulation method and ab initio thermodynamics to explore the low-energy configurations of metal surface reconstructions, which takes the surface dynamics under the reactive environment into account. We systematically simulate 13 Au surfaces ((100), (110), (111), (210), (211), (221), (310), (311), (320), (321), (322), (331), and (332)) in the CO environment and identify 19 candidate reconstruction patterns driven by CO adsorption. The breakup of the original surfaces is attributed to the lateral interactions among the nearest-neighboring adsorbates. This work provides an efficient approach to unveil the reconstructed metal surface structures in reactive environments for guiding the experiments.

Multiscale Kinetic Monte Carlo Simulation of Self-Organized Growth of GaN/AlN Quantum Dots

A three-dimensional kinetic Monte Carlo methodology is developed to study the strained epitaxial growth of wurtzite GaN/AlN quantum dots. It describes the kinetics of effective GaN adatoms on an hexagonal lattice. The elastic strain energy is evaluated by a purposely devised procedure: first, we take advantage of the fact that the deformation in a lattice-mismatched heterostructure is equivalent to that obtained by assuming that one of the regions of the system is subjected to a properly chosen uniform stress (Eshelby inclusion concept), and then the strain is obtained by applying the Green's function method. The standard Monte Carlo method has been modified to implement a multiscale algorithm that allows the isolated adatoms to perform long diffusion jumps. With these state-of-the art modifications, it is possible to perform efficiently simulations over large areas and long elapsed times. We have taylored the model to the conditions of molecular beam epitaxy under N-rich conditions. The corresponding simulations reproduce the different stages of the Stranski-Krastanov transition, showing quantitative agreement with the experimental findings concerning the critical deposition, and island size and density. The influence of growth parameters, such as the relative fluxes of Ga and N and the substrate temperature, is also studied and found to be consistent with the experimental observations. In addition, the growth of stacked layers of quantum dots is also simulated and the conditions for their vertical alignment and homogenization are illustrated. In summary, the developed methodology allows one to reproduce the main features of the self-organized quantum dot growth and to understand the microscopic mechanisms at play.

Adsorption and Absorption Energies of Hydrogen with Palladium

Thermal recombinative desorption rates of HD on Pd(111) and Pd(332) are reported from transient kinetic experiments performed between 523 and 1023 K. A detailed kinetic model accurately describes the competition between recombination of surface-adsorbed hydrogen and deuterium atoms and their diffusion into the bulk. By fitting the model to observed rates, we derive the dissociative adsorption energies (E _0, ads ^H₂ = 0.98 eV; E _0, ads ^D₂ = 1.00 eV; E _0, ads ^HD = 0.99 eV) as well as the classical dissociative binding energy ϵ_ads = 1.02 ± 0.03 eV, which provides a benchmark for electronic structure theory. In a similar way, we obtain the classical energy required to move an H or D atom from the surface to the bulk (ϵ_sb = 0.46 ± 0.01 eV) and the isotope specific energies, E _0, sb ^H = 0.41 eV and E _0, sb ^D = 0.43 eV. Detailed insights into the process of transient bulk diffusion are obtained from kinetic Monte Carlo simulations.

High-Throughput Screening of Bicationic Redox Materials for Chemical Looping Ammonia Synthesis

Ammonia recently has gained increasing attention as a carrier for the efficient and safe usage of hydrogen to further advance the hydrogen economy. However, there is a pressing need to develop new ammonia synthesis techniques to overcome the problem of intense energy consumption associated with the widely used Haber-Bosch process. Chemical looping ammonia synthesis (CLAS) is a promising approach to tackle this problem, but the ideal redox materials to drive these chemical looping processes are yet to be discovered. Here, by mining the well-established MP database, the reaction free energies for CLAS involving 1699 bicationic inorganic redox pairs are screened to comprehensively investigate their potentials as efficient redox materials in four different CLAS schemes. A state-of-the-art machine learning strategy is further deployed to significantly widen the chemical space for discovering the promising redox materials from more than half a million candidates. Most importantly, using the three-step H2 O-CL as an example, a new metric is introduced to determine bicationic redox pairs that are "cooperatively enhanced" compared to their corresponding monocationic counterparts. It is found that bicationic compounds containing a combination of alkali/alkaline-earth metals and transition metal (TM)/post-TM/metalloid elements are compounds that are particularly promising in this respect.